Hydro-treating of Asphaltenes in Supercritical Toluene with MgO-Supported Fe, Ni, NiMo, and CoMo Catalysts
Bibliographic record
Abstract
For the first time, the AVTB-derived asphaltenes (containing very high concentrations of sulfur (7.70 wt % S) and nitrogen (1.08 wt % N)) were hydrotreated over novel MgO-supported Fe, Ni, NiMo, and CoMo catalysts in supercritical toluene at 380 °C under hydrogen of 5 MPa (cold pressure) in a batch reactor. The supercritical toluene alone without any catalyst was found to be effective for conversion of the highly aromatic asphaltenes (AS) to less aromatic maltenes (MA) through a thermal cracking mechanism. All the MgO-supported catalysts proved to be effective for hydro-conversion of AS to MA in supercritical toluene. The activities of these catalysts for AS conversion showed the order of sequence of Fe/MgO < Ni/MgO < CoMo/MgO < NiMo/MgO. Among all the MgO-supported catalysts tested, NiMo/MgO was found to be the most active one for promoting the removal of sulfur and AS. The sulfur/nitrogen/AS removal efficiencies for the NiMo/MgO catalyst increased with the treatment time, and the efficiencies at 120 min of treatment attained as high as 43%, 30%, and 70%, respectively. The MgO-supported catalysts showed excellent performance for preventing and retarding the formation of highly condensed aromatics or coke in the hydro-treatment of AS in supercritical toluene. With the presence of NiMo/MgO catalyst, hydro-treating AS in the supercritical toluene for a reaction time up to 120 min produced a strikingly low yield (<0.7%) of toluene-insoluble product.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".